Human Splicing Finder: an online bioinformatics tool to predict splicing signals

Abstract

Thousands of mutations are identified yearly. Although many directly affect protein expression, an increasing proportion of mutations is now believed to influence mRNA splicing. They mostly affect existing splice sites, but synonymous, non-synonymous or nonsense mutations can also create or disrupt splice sites or auxiliary cis-splicing sequences. To facilitate the analysis of the different mutations, we designed Human Splicing Finder (HSF), a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-beta Serine-Arginine proteins and the hnRNP A1 ribonucleoprotein. We also developed new Position Weight Matrices to assess the strength of 5' and 3' splice sites and branch points. We evaluated HSF efficiency using a set of 83 intronic and 35 exonic mutations known to result in splicing defects. We showed that the mutation effect was correctly predicted in almost all cases. HSF could thus represent a valuable resource for research, diagnostic and therapeutic (e.g. therapeutic exon skipping) purposes as well as for global studies, such as the GEN2PHEN European Project or the Human Variome Project.

Figure 1.

Branch point matrix. The size of each nucleotide is proportional to its weight in the position weight matrix. Nucleotides above the base line have positive values while nucleotides below have negative values.

Figure 2.

New position weight matrices of recognition motifs for proteins involved in splicing. (A) hnRNP A1; (B) Tra2-β and (C) 9G8.

Figure 3.

Distribution of CVs for (A) 3′ and (B) 5′ natural splice sites (5′ss and 3′ss). Data extracted from the Ensembl dataset (release 44, http://april2007.archive.ensembl.org) (20) using the HSF algorithm.